R Markdown
library(tidyverse)
## ── Attaching packages ────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
surveys_complete <- read_csv("data/surveys_complete.csv")
## Parsed with column specification:
## cols(
## record_id = col_double(),
## month = col_double(),
## day = col_double(),
## year = col_double(),
## plot_id = col_double(),
## species_id = col_character(),
## sex = col_character(),
## hindfoot_length = col_double(),
## weight = col_double(),
## genus = col_character(),
## species = col_character(),
## taxa = col_character(),
## plot_type = col_character()
## )
ggplot(data = surveys_complete)

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length))

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
geom_point()

install.packages("hexbin")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
library("hexbin")
ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
geom_point()

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
geom_point(alpha = 0.1)

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
geom_point(alpha = 0.1, color = "blue")

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
geom_point(alpha = 0.1, aes(color = species_id))

ggplot(data = surveys_complete,
mapping = aes(x = species_id, y = weight)) +
geom_point(aes(color = plot_type))

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_boxplot()

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_boxplot(alpha = 0) +
geom_jitter(alpha = 0.3, color = "tomato")

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_violin() +
scale_y_log10()

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = hindfoot_length)) +
geom_violin() +
scale_y_log10() +
geom_jitter(alpha = 0.3, color = "tomato")

yearly_counts <- surveys_complete %>%
count(year, genus)
ggplot(data = yearly_counts, aes(x = year, y = n)) +
geom_line()

ggplot(data = yearly_counts, aes(x = year, y = n, group = genus)) +
geom_line()

ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
geom_line()

yearly_counts_graph <- surveys_complete %>%
count(year, genus) %>%
ggplot(mapping = aes(x = year, y = n, color = genus)) +
geom_line()
yearly_counts_graph

ggplot(data = yearly_counts, aes(x = year, y = n)) +
geom_line() +
facet_wrap(facets = vars(genus))

yearly_sex_counts <- surveys_complete %>%
count(year, genus, sex)
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(facets = vars(genus))

ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(rows = vars(sex), cols = vars(genus))

# One column, facet by rows
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(rows = vars(genus))

# One row, facet by column
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(cols = vars(genus))

ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
theme_bw()

yearly_weight <- surveys_complete %>%
group_by(year, species_id) %>%
summarize(avg_weight = mean(weight))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(data = yearly_weight, mapping = aes(x=year, y=avg_weight)) +
geom_line() +
facet_wrap(vars(species_id)) +
theme_light()

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
labs(title = "Observed genera through time",
x = "Year of observation",
y = "Number of individuals") +
theme_bw()

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
labs(title = "Observed genera through time",
x = "Year of observation",
y = "Number of individuals") +
theme_bw() +
theme(text=element_text(size = 16))

ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
labs(title = "Observed genera through time",
x = "Year of observation",
y = "Number of individuals") +
theme_bw() +
theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
strip.text = element_text(face = "italic"),
text = element_text(size = 16))

grey_theme <- theme(axis.text.x = element_text(colour="grey20", size = 12,
angle = 90, hjust = 0.5,
vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
text=element_text(size = 16))
ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
geom_boxplot() +
grey_theme

ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
geom_rug() +
grey_theme
